About — OmniPrep

Overview

OmniPrep is a unified neuroimaging pipeline that standardizes and preprocesses structural (sMRI), functional (fMRI), and diffusion (dMRI) MRI data within a single, coherent workflow. Built on Nextflow, it is containerized, resumable, and designed to scale from a single workstation to large HPC clusters and the cloud.

Beyond preprocessing, OmniPrep generates analysis-ready derivatives: atlas- and ROI-level morphometric features, voxelwise and ROI functional metrics (ReHo, ALFF/fALFF, INT, time series, functional connectivity), and diffusion-derived tractography and structural connectomes — all with built-in quality control.

Design principles

Unified

One command, one configuration surface, one output convention across sMRI, fMRI, and dMRI.

Scalable

Nextflow orchestrates work across local, cluster, and cloud executors without changing the interface.

Robust

Automatic age detection, adaptive templates and distortion-correction strategies, resumable runs, and selective error tolerance.

Reproducible

Containerized dependencies, BIDS input, standardized derivatives, and versioned QC artifacts.

Roadmap

Milestone	Status	Notes
Multimodal preprocessing (sMRI / fMRI / dMRI)	active	Core workflow on Nextflow with Docker & Singularity.
Atlas & ROI derivatives	active	Morphometry, ROI time series, FC & structural connectomes.
Automated QC	active	Run report, trace, timeline, motion scrubbing.
Interactive web UI	planned	Streamlit-based configuration front-end.
Expanded atlas catalog	planned	Additional cortical & subcortical parcellations.
Group-level workflow	planned	Cohort-level aggregation and statistics.

Citation

If you use OmniPrep in your research, please cite the project (and the upstream tools it builds upon). A formal citation will be added upon publication.

bibtex

@software{omniprep,
  title   = {OmniPrep: A Unified, Scalable, and Robust Pipeline for Large-Scale Multimodal MRI},
  author  = {OmniPrep Contributors},
  year    = {2026},
  url     = {$https://github.com/uxhao-o/omniprep-website}
}

Acknowledgements

OmniPrep builds on and is deeply indebted to the open neuroimaging community. Key dependencies and inspirations include:

FreeSurfer — cortical reconstruction & segmentation
fMRIPrep / NiPreps — preprocessing methodology
Nextflow — workflow orchestration
FSL & MRtrix3 — distortion correction, tractography, connectomics
BIDS — the data standard OmniPrep consumes and respects
Power et al. motion frame detection methodology

Contributing

OmniPrep is developed openly. Contributions — bug reports, feature requests, documentation improvements, and pull requests — are welcome. Please follow the project's contribution guidelines when submitting changes.

Contact

For questions, feedback, or collaboration, open an issue on the project repository or reach out to the maintainers. Stay tuned for community channels as the project matures.

About OmniPrep